Zobrazeno 1 - 10
of 9 021
pro vyhledávání: '"Guo, Qi"'
Contribution evaluation in federated learning (FL) has become a pivotal research area due to its applicability across various domains, such as detecting low-quality datasets, enhancing model robustness, and designing incentive mechanisms. Existing co
Externí odkaz:
http://arxiv.org/abs/2407.02073
Autor:
Zhao, Zhengyue, Zhang, Xiaoyun, Xu, Kaidi, Hu, Xing, Zhang, Rui, Du, Zidong, Guo, Qi, Chen, Yunji
With the widespread application of Large Language Models (LLMs), it has become a significant concern to ensure their safety and prevent harmful responses. While current safe-alignment methods based on instruction fine-tuning and Reinforcement Learnin
Externí odkaz:
http://arxiv.org/abs/2406.16743
Text-to-image diffusion models can create realistic images based on input texts. Users can describe an object to convey their opinions visually. In this work, we unveil a previously unrecognized and latent risk of using diffusion models to generate i
Externí odkaz:
http://arxiv.org/abs/2406.15863
Autor:
Wang, Yidong, Guo, Qi, Yao, Wenjin, Zhang, Hongbo, Zhang, Xin, Wu, Zhen, Zhang, Meishan, Dai, Xinyu, Zhang, Min, Wen, Qingsong, Ye, Wei, Zhang, Shikun, Zhang, Yue
This paper introduces AutoSurvey, a speedy and well-organized methodology for automating the creation of comprehensive literature surveys in rapidly evolving fields like artificial intelligence. Traditional survey paper creation faces challenges due
Externí odkaz:
http://arxiv.org/abs/2406.10252
Autor:
Gao, Haihan, Zhang, Rui, Yi, Qi, Yao, Hantao, Li, Haochen, Guo, Jiaming, Peng, Shaohui, Gao, Yunkai, Wang, QiCheng, Hu, Xing, Wen, Yuanbo, Zhang, Zihao, Du, Zidong, Li, Ling, Guo, Qi, Chen, Yunji
Overfitting in RL has become one of the main obstacles to applications in reinforcement learning(RL). Existing methods do not provide explicit semantic constrain for the feature extractor, hindering the agent from learning a unified cross-domain repr
Externí odkaz:
http://arxiv.org/abs/2406.03250
Autor:
Guo, Yuxuan, Peng, Shaohui, Guo, Jiaming, Huang, Di, Zhang, Xishan, Zhang, Rui, Hao, Yifan, Li, Ling, Tian, Zikang, Gao, Mingju, Li, Yutai, Gan, Yiming, Liang, Shuai, Zhang, Zihao, Du, Zidong, Guo, Qi, Hu, Xing, Chen, Yunji
Building open agents has always been the ultimate goal in AI research, and creative agents are the more enticing. Existing LLM agents excel at long-horizon tasks with well-defined goals (e.g., `mine diamonds' in Minecraft). However, they encounter di
Externí odkaz:
http://arxiv.org/abs/2405.15414
The stellar-to-halo mass relation (SHMR) is a fundamental relationship between galaxies and their host dark matter haloes. In this study, we examine the scatter in this relation for primary galaxies in the semi-analytic L-Galaxies model and two cosmo
Externí odkaz:
http://arxiv.org/abs/2405.15191
With the rapid growth in the number of large language model (LLM) users, it is difficult for bandwidth-constrained cloud servers to simultaneously process massive LLM services in real-time. Recently, edge-cloud infrastructures have been used to impro
Externí odkaz:
http://arxiv.org/abs/2405.14636
The visible spectrum of Mo$^{15+}$ ions was measured using a high-temperature superconducting electron-beam ion trap at the Shanghai EBIT Laboratory, with an electron beam energy $E_{e}$=400 eV, significantly lower than the ionization potential (IP=5
Externí odkaz:
http://arxiv.org/abs/2405.00893
Targeted transfer-based attacks involving adversarial examples pose a significant threat to large visual-language models (VLMs). However, the state-of-the-art (SOTA) transfer-based attacks incur high costs due to excessive iteration counts. Furthermo
Externí odkaz:
http://arxiv.org/abs/2404.10335